Data science tools have revolutionized the way farmers and agricultural professionals approach their work. Python data analysis tools, such as pandas and seaborn, enable farmers to make data-driven decisions using soil, water, and economic data accounts. Pandas is a Python library used to simplify handling large sets of data. Seaborn is a data visualization library used to quickly create graphs.
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Data science tools have revolutionized the way farmers and agricultural professionals approach their work. Python data analysis tools, such as pandas and seaborn, enable farmers to make data-driven decisions using soil, water, and economic data accounts. Pandas is a Python library used to simplify handling large sets of data. Seaborn is a data visualization library used to quickly create graphs.
Data science tools have revolutionized the way farmers and agricultural professionals approach their work. Python data analysis tools, such as pandas and seaborn, enable farmers to make data-driven decisions using soil, water, and economic data accounts. Pandas is a Python library used to simplify handling large sets of data. Seaborn is a data visualization library used to quickly create graphs.
This hands-on guided project will prepare you to handle agricultural datasets using these Python tools. You will develop job-ready skills, like how to download, prepare, analyze, and visualize data using Python libraries, including pandas and seaborn. You will learn how to build a trend line in order to forecast future trends, and finally, you will learn how to create interactive maps which show data change over time.
You will be provided with access to a Cloud-based IDE, which has all of the required software, including Python, pre-installed. All you need is a recent version of a modern web browser to complete this project.
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Learning objectives
- After completing this project, you will be able to:
- Read a csv file
- Convert the csv file to a dataframe
- Preprocess the data
- Perform statistical analysis of the data and display various summary statistics
- Visualize data using pandas and seaborn
- Build interactive maps using plotly
- After completing this project, you will be able to:
- Read a csv file
- Convert the csv file to a dataframe
- Preprocess the data
- Perform statistical analysis of the data and display various summary statistics
- Visualize data using pandas and seaborn
- Build interactive maps using plotly
Good to know
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Activities
Read 'Python Data Science Handbook' by Jake VanderPlas
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Gain a comprehensive understanding of data science using Python, a key skill for this course.
View
Python Data Science Handbook: Essential Tools...
on Amazon
Show steps
-
Purchase or borrow the book
-
Read the chapters relevant to the course material
-
Take notes and highlight important concepts
Review Python programming fundamentals
Show steps
Ensure you have a strong foundation in Python programming, a prerequisite for this course.
Browse courses on
Python
Show steps
-
Review online tutorials or documentation on Python basics
-
Complete practice exercises to reinforce your understanding
-
Consider taking a refresher course or workshop
Join a study group to discuss course concepts
Show steps
Engage with fellow students to discuss course concepts, clarify doubts, and reinforce your understanding.
Show steps
-
Find a study group or create one with classmates
-
Meet regularly to discuss the course material
-
Work together on assignments and projects
Five other activities
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Show all eight activities
Find a mentor with experience in Python data science
Show steps
Seek guidance from an experienced professional to enhance your understanding of Python data science, a key skill for this course.
Browse courses on
Python
Show steps
-
Attend industry events or online forums to connect with professionals
-
Reach out to professors or colleagues for recommendations
-
Request informational interviews to learn about their experiences
Complete the 'Pandas for Data Analysis' tutorial
Show steps
Gain a strong foundation in using Python pandas for data analysis and visualization, a key skill for this course.
Browse courses on
Data Analysis
Show steps
-
Find the 'Pandas for Data Analysis' tutorial online
-
Follow the tutorial step-by-step, completing all the exercises
-
Refer to the tutorial during the course to reinforce your learning
Attend a Python pandas and seaborn workshop
Show steps
Develop job-ready skills, like how to download, prepare, analyze, and visualize data using Python libraries, including pandas and seaborn.
Browse courses on
Seaborn
Show steps
-
Register for a workshop that covers Python pandas and seaborn
-
Attend the workshop and participate in the hands-on exercises
-
Ask questions and take notes during the workshop
Practice analyzing and visualizing agricultural data
Show steps
Sharpen your data analysis and visualization skills by working with real-world agricultural data, as you will do in this course.
Browse courses on
Data Analysis
Show steps
-
Find a dataset related to agriculture
-
Use Python pandas to load and clean the data
-
Perform exploratory data analysis to understand the data
-
Visualize the data using Seaborn to identify patterns and trends
Create a data dashboard to visualize agricultural data
Show steps
Demonstrate your understanding of data visualization by creating an interactive dashboard that showcases agricultural data, as you will do in this course.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset related to agriculture
-
Use Python pandas and Seaborn to analyze and visualize the data
-
Design and develop a data dashboard using a tool like Plotly
-
Share your dashboard with others and gather feedback
Read 'Python Data Science Handbook' by Jake VanderPlas
Show steps
Gain a comprehensive understanding of data science using Python, a key skill for this course.
View
Python Data Science Handbook: Essential Tools...
on Amazon
Show steps
- Purchase or borrow the book
- Read the chapters relevant to the course material
- Take notes and highlight important concepts
Review Python programming fundamentals
Show steps
Ensure you have a strong foundation in Python programming, a prerequisite for this course.
Browse courses on
Python
Show steps
- Review online tutorials or documentation on Python basics
- Complete practice exercises to reinforce your understanding
- Consider taking a refresher course or workshop
Join a study group to discuss course concepts
Show steps
Engage with fellow students to discuss course concepts, clarify doubts, and reinforce your understanding.
Show steps
- Find a study group or create one with classmates
- Meet regularly to discuss the course material
- Work together on assignments and projects
Find a mentor with experience in Python data science
Show steps
Seek guidance from an experienced professional to enhance your understanding of Python data science, a key skill for this course.
Browse courses on
Python
Show steps
- Attend industry events or online forums to connect with professionals
- Reach out to professors or colleagues for recommendations
- Request informational interviews to learn about their experiences
Complete the 'Pandas for Data Analysis' tutorial
Show steps
Gain a strong foundation in using Python pandas for data analysis and visualization, a key skill for this course.
Browse courses on
Data Analysis
Show steps
- Find the 'Pandas for Data Analysis' tutorial online
- Follow the tutorial step-by-step, completing all the exercises
- Refer to the tutorial during the course to reinforce your learning
Attend a Python pandas and seaborn workshop
Show steps
Develop job-ready skills, like how to download, prepare, analyze, and visualize data using Python libraries, including pandas and seaborn.
Browse courses on
Seaborn
Show steps
- Register for a workshop that covers Python pandas and seaborn
- Attend the workshop and participate in the hands-on exercises
- Ask questions and take notes during the workshop
Practice analyzing and visualizing agricultural data
Show steps
Sharpen your data analysis and visualization skills by working with real-world agricultural data, as you will do in this course.
Browse courses on
Data Analysis
Show steps
- Find a dataset related to agriculture
- Use Python pandas to load and clean the data
- Perform exploratory data analysis to understand the data
- Visualize the data using Seaborn to identify patterns and trends
Create a data dashboard to visualize agricultural data
Show steps
Demonstrate your understanding of data visualization by creating an interactive dashboard that showcases agricultural data, as you will do in this course.
Browse courses on
Data Visualization
Show steps
- Choose a dataset related to agriculture
- Use Python pandas and Seaborn to analyze and visualize the data
- Design and develop a data dashboard using a tool like Plotly
- Share your dashboard with others and gather feedback
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